Mastering Object-Oriented Programming in Python

From Data Structures to Building Systems: Diving into Python OOP! 🐍 Today was a powerhouse of learning. I transitioned from organizing data in Dictionaries to understanding the core philosophy of Object-Oriented Programming (OOP). It’s not just about writing code anymore; it’s about building scalable and reusable systems. Here’s a breakdown of today’s deep dive: 📖 Dictionaries: Mastered key-value pair mapping for efficient data retrieval. 🏗️ Classes & Objects: Learned how to create blueprints (Classes) and bring them to life as real-world entities (Objects). ⚙️ Constructors (__init__): Understanding how to initialize object state the moment it's created. 🧬 Inheritance & Its Types: Explored how to pass attributes and methods from one class to another—reducing redundancy using Single, Multiple, and Multilevel Inheritance. 🎭 Polymorphism: The beauty of "Many Forms." Learning how different classes can be treated as instances of the same general class through method overriding and overloading. OOP has completely changed my perspective on how to structure a project. I'm excited to start implementing these design patterns into my FastAPI backend development! #Python #OOP #SoftwareEngineering #CodingJourney #ObjectOrientedProgramming #BackendDeveloper #CleanCode #ContinuousLearning #TechCommunity #PythonProgramming

One of the most common questions beginners ask is: "I’ve learned Python basics... now what?" The beauty of Python isn't just in the syntax; it’s in the incredible ecosystem of libraries that allow you to pivot into almost any field. Whether you want to build AI agents, automate your boring tasks, or dive deep into data, there is a "formula" for it. Here is a quick breakdown of the Python combinations that power the industry today: For Data Fanatics: Python + Pandas = Data Analysis 📊 For AI Pioneers: Python + LangChain = AI Agents 🤖 For Web Architects: Python + Django/Flask = Web Development 🌐 For Automation Kings: Python + Selenium/Airflow = Workflow Magic ⚙️ For Visual Storytellers: Python + Matplotlib = Data Visualization 📈 Which "formula" are you currently working on? I’m personally diving deep into the data side of things, but the more I see what’s possible with Streamlit and FastAPI, the more I realize the possibilities are endless. Let’s discuss in the comments! What’s your favorite Python library to work with right now? #Python #DataScience #WebDevelopment #Programming #TechCommunity #Automation #LearningToCode #DataAnalytics #SoftwareEngineering

DERA has published a set of Python code examples to make it easier for analysts, researchers, and developers to access and work with the SEC’s XBRL Financial Statement and Notes Data Sets: https://lnkd.in/gpWuXJZD   The GitHub repository walks through:  • Reading quarterly data into Pandas • Joining and analyzing numeric, dimensional, narrative, and custom facts • Visualizing results • Working with multiple datasets and exporting outputs   Code, notebooks, and setup instructions are all available in the link.

Great foundation from the SEC DERA team. I was able to modernize this in an afternoon, swapping Pandas for Polars with lazy evaluation, adding DuckDB for direct SQL queries on the TSV files, and a benchmark showing the speed difference on real XBRL data. 4.20.2026 1900 PST: This update improves integration with external data pipelines./Notes and R-based incremental downloader + DuckDB/Parquet workflow, which served as a strong reference point for data ingestion design patterns. Fork with improvements here: https://lnkd.in/g58ESerZ Happy to contribute anything back if useful. #Code #SEC #finance #data #AI #trading #Stockmarket #SQL #XBRL #fullstack #financialservices

While preparing a risk management module, I came across a very useful resource that deserves more visibility. This repository by the U.S. Securities and Exchange Commission (SEC) provides Python-based tools to work with structured financial datasets derived from company filings. What makes it valuable? Access to SEC Financial Statement datasets Structured data extracted from XBRL filings Ready-to-use Python workflows using Pandas Ideal for financial modeling, empirical research, and analytics For anyone working on financial research, sustainability reporting, valuation, or data-driven finance projects, this can significantly reduce the effort required to clean and structure raw filings. #Finance #FinancialModeling #DataAnalytics #Python #Research #SEC #XBRL #FinTech #OpenData

Good release from DERA. The broader point is not just access to data. It is making public market information more usable, more scalable, and easier to work with in modern analytical workflows. That is how transparency starts to compound.

Check it out: Automate Peer Benchmarking: Instantly extract and compare financial KPIs across entire industries to see how competitors stack up without manual data entry. Uncover Footnote Insights: Search thousands of narrative disclosures simultaneously to flag "hidden" risks like litigation, supply chain shifts, or aggressive accounting. Build Data-Driven Dashboards: Transform raw SEC filings into clean, visual trends to identify long-term sector shifts and high-growth opportunities.

𝗜𝗧𝗘𝗥𝗔𝗧𝗢𝗥𝗦 𝗩𝗦 𝗚𝗘𝗡𝗘𝗥𝗔𝗧𝗢𝗥𝗦 𝗜𝗡 𝗣𝗬𝗧𝗛𝗢𝗡 If you want to write efficient Python code, you need to understand the difference between iterators and generators. They help you loop through data, but they work differently in terms of memory usage and performance. You use iterators and generators in data science, backend systems, APIs, and large datasets. Mastering this concept will improve your Python skills. Iteration means accessing elements of a collection one by one. Python uses the iterator protocol, which includes: - Returning iterator object - Returning next value - Sequential data access An iterator is an object that allows you to traverse elements one at a time. It implements the iterator protocol. A generator is a simpler way to create iterators using a function and the yield keyword. Generators use yield instead of return. Here are the key differences: - Iterators are created using classes - Iterators have more code and control - Iterators use more memory - Iterators are controlled manually - Generators are simpler and use less memory - Generators pause execution and continue from where they paused Use iterators when you need full control. Use generators when working with large datasets. You can use generators for: - Large file processing - Data pipelines - API pagination - Infinite sequences Generators are faster due to lazy evaluation and reduced memory usage. Yield allows pausing and resuming execution. In most cases, generators can replace iterators. Understanding iterators and generators is crucial for writing high-performance applications. Use generators for better performance. Replace large lists with generators in your projects to see performance improvements. Source: https://lnkd.in/gYXKnBS3 Optional learning community: https://t.me/GyaanSetuAi

I used to think Data Structures were just a hard exam topic. Then StemLink lectures showed me how they actually work in Python — and everything changed. 🐍 Here's what I learned, broken down simply 👇 --- 🔷 The 4 main data structures in Python: 📋 Lists → ordered, mutable, most used   mylist = []   mylist.append("item")  # add to end   mylist.sort()      # sort in place 📖 Dictionaries → key-value pairs, O(1) lookup   user = {"name": "Abiya", "age": 20} 🔸 Tuples → like lists but NOT mutable   coords = (6.9, 79.8)  # can't change this 🔹 Sets → unique values only, no duplicates   tags = {"python", "dsa", "python"} # stores only once --- 🔷 How data lives inside structures: Everything stored inside these is called an Element. Elements are ordered in groups → accessed using Indexes. mylist[0]    # first item mylist[-1]    # last item mylist[0:3]   # slice from 0 to 2 --- 🔷 How we move through data: Loops + Indexes work together to iterate through elements. for elem in mylist:   # loop through every item   print(elem) mylist[int] = x     # modify by assignment --- 🔷 The big insight: Lists are the most popular data structure in Python. They connect everything — elements, loops, indexes, methods, and assignment — all in one place. Once you understand Lists deeply, the rest makes sense. These fundamentals go directly into the projects I build. Not just theory. Real code. Which Python data structure do you use the most? 👇 #Python #DSA #DataStructures #StemLink #IITColombo #LearnToCode #CS #StudentDeveloper #BuildInPublic #Programming

**Go or Python: Which one fuels your data workflow better? 🤔🚀** When it comes to scaling massive datasets or rapid prototyping, your choice of language can elevate—or complicate—your entire project. Our latest guide breaks down the strengths, trade-offs, and best use cases for both Go and Python, so you can make smarter, faster decisions for your data stack. Ready to find out which language really fits your workflow—and why most teams get this decision wrong? 👇 Read the full guide here: https://lnkd.in/dhhU2uaF